Archives de catégorie : LLM

Enhancing FROG with Insights from WeightWatcher: A Deep Dive into Neural Network Analysis

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The FROG (Frobenius-guided Relevance Optimization with Guided noise) method has shown promise in efficient fine-tuning of large language models. However, by incorporating some key ideas from WeightWatcher, we can potentially improve FROG’s effectiveness and broaden its analytical capabilities. Let’s explore the most relevant concepts from WeightWatcher that could enhance FROG.

1. Power Law Exponent Analysis

WeightWatcher’s use of power law exponents (α) to analyze weight matrices offers a powerful tool for assessing layer quality without access to training or test data.

How it works:

  • WeightWatcher computes eigenvalues for each layer’s weight matrix using Singular Value Decomposition (SVD).
  • It then fits the eigenvalue density to a truncated power law distribution, deriving the power law exponent α.
  • Typically, α values range from 2 to 6, with lower values indicating better quality.

Potential FROG Enhancement:

FROG could incorporate this power law exponent analysis to refine its weight importance scoring. Instead of relying solely on the current Sij scoring, FROG could use a combination of Sij and α to determine weight importance. This could lead to more nuanced selection of weights for fine-tuning.

2. Layer-wise Quality Metrics

WeightWatcher provides detailed layer-by-layer analysis, offering insights into the quality of individual layers within a network.

Key Metrics:

  • α (Power Law Exponent)
  • Log Spectral Norm
  • Log Frobenius Norm

FROG Application:

By adopting these layer-wise metrics, FROG could:

  1. Identify layers that are most critical for fine-tuning.
  2. Adjust its weight selection strategy based on layer quality.
  3. Provide more granular insights into model architecture and potential areas for improvement.

3. Model-wide Quality Assessment

WeightWatcher calculates an average α-hat metric, which correlates well with model performance across various architectures.

FROG Integration:

  • Implement a similar model-wide metric in FROG to quickly assess overall model quality before and after fine-tuning.
  • Use this metric to guide the extent of fine-tuning needed or to compare different fine-tuning strategies.

4. Detecting Overparameterization

WeightWatcher can identify overparameterized layers by looking for unusually high α values (above 6).

FROG Enhancement:

  • Incorporate overparameterization detection into FROG’s analysis.
  • Use this information to potentially prune or more aggressively fine-tune overparameterized layers.
  • Adjust the fine-tuning strategy based on the degree of overparameterization in different parts of the model.

5. Correlation Flow Analysis

WeightWatcher examines how information flows through the network by analyzing correlations between layers.

Potential FROG Application:

  • Implement a similar correlation analysis in FROG.
  • Use this to identify critical pathways in the network that should be preserved or enhanced during fine-tuning.
  • Adjust weight selection strategies to maintain or improve these important correlations.

6. Scale Collapse Detection

WeightWatcher can identify potential problems in model distillation by detecting scale collapse.

FROG Integration:

  • Implement scale collapse detection in FROG.
  • Use this to guide fine-tuning strategies that avoid degradation of model performance, especially when adapting models to new tasks or domains.

Conclusion

By incorporating these ideas from WeightWatcher, FROG could evolve into a more comprehensive tool for model analysis and fine-tuning. The enhanced FROG would not only select important weights for fine-tuning but also provide deeper insights into model quality, architecture, and potential areas for improvement.

The integration of power law exponent analysis, layer-wise quality metrics, and overparameterization detection could lead to more targeted and effective fine-tuning strategies. Meanwhile, the addition of correlation flow analysis and scale collapse detection could help preserve critical model structures during the fine-tuning process.

These enhancements would position FROG as a more robust tool for efficient and insightful fine-tuning of large language models, combining the strengths of both FROG and WeightWatcher approaches.

Sources
[2] Build better Large Language Models with WeightWatcher https://gradientflow.com/build-better-large-language-models-with-weightwatcher/
[3] WeightWatcher: Data-Free Diagnostics for Deep Learning https://weightwatcher.ai
[4] WeightWatcher: Empirical Quality Metrics for Deep Neural Networks https://calculatedcontent.com/2020/02/16/weightwatcher-empirical-quality-metrics-for-deep-neural-networks/

Accelerating Your Model Evaluation and Fine-tuning with SFR-Judge

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  • SFR-Judge is a family of three judge models (8B, 12B, and 70B parameters) developed by Salesforce AI Research.
  • These models are built using Meta Llama 3 and Mistral NeMO, designed to evaluate outputs from large language models (LLMs).
  • SFR-Judge can perform three types of evaluation tasks:
    • Pairwise comparisons
    • Single ratings on a Likert scale
    • Binary classification.
  • The models are trained to provide explanations for their judgments, enhancing transparency.
  • SFR-Judge outperformed other open-source and proprietary judge models in 10 out of 13 benchmarks.
  • The models demonstrated lower bias and higher consistency compared to competitive judge models.
  • SFR-Judge models ranked first, second, and fourth on the RewardBench leaderboard for generative judge models.
  • These models are the first to achieve over 90% accuracy on RewardBench.
  • SFR-Judge can be used for auto-evaluation and as reward models for reinforcement learning from human feedback (RLHF).
  • Downstream models improved with SFR-Judge showed better performance on the AlpacaEval-2 instruction following benchmark.
  • The research paper and code (coming soon) are available for further explorations.

Blog reference

paper

Publié dans LLM | Marqué avec

Mastering the Art of Prompt Engineering: 20 Essential Tips

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Prompt engineering has become a crucial skill in the era of advanced language models. Whether you’re a developer, researcher, or enthusiast working with AI, understanding how to effectively communicate with these models can significantly enhance your results. Here are 20 key tips to improve your prompt engineering skills:

Communication and Clarity

  1. Communicate clearly and concisely: Precision in your language is paramount when interacting with AI models.
  2. Give specific instructions: Provide clear, concise directions that are tailored to your particular task.
  3. Anticipate misinterpretations: Consider how the model might misunderstand your prompts and preemptively address potential issues.

Experimentation and Learning

  1. Iterate and experiment: Don’t be afraid to try different approaches with your prompts.
  2. Learn from mistakes: Carefully analyze the model’s outputs to understand where improvements can be made.
  3. Push boundaries: Challenge your assumptions about the model’s capabilities.

Understanding the Model

  1. Think of it as a knowledgeable temp: Imagine the model as a highly informed temporary worker who needs specific guidance.
  2. Provide context: Don’t hesitate to give more background information than you think is necessary.
  3. Avoid forcing personas: Let the model’s natural capabilities shine instead of trying to make it play a specific role.

Effective Prompting Techniques

  1. Use illustrative examples: Provide examples to clarify your task, but be mindful not to overwhelm the model.
  2. Diversify your examples: Use instances that differ from the data the model will actually work with.
  3. Mind your language: While good grammar and punctuation are helpful, they’re not strictly necessary for the model to understand you.
  4. Consider the model as an imitator: Remember that the AI will attempt to mimic your writing style.
  5. Leverage other models: Use different AI models to help craft your prompts.

Respecting the Model’s Nature

  1. Treat it with respect: Approach the model as if it were an intelligent and capable entity.
  2. Simulate the model’s perspective: Try to put yourself in the AI’s position to better understand its responses.
  3. Be creative with concepts: Don’t shy away from introducing new ideas to convey your intentions to the model.
  4. Explain as if to a layperson: Frame your prompts as if you’re explaining the topic to an educated person unfamiliar with the subject.
  5. Provide an « out »: Give the model a clear way to respond when it encounters unexpected inputs.
  6. Externalize your thinking: Try to transfer your thought process into the prompt for the model to follow.

By incorporating these tips into your prompt engineering practice, you can significantly improve your interactions with AI language models. Remember that the effectiveness of these strategies may vary depending on the specific task and model you’re working with. Continuous experimentation and refinement of your approach will lead to the best results in prompt engineering.

Sources

Distilling Knowledge from Large LLMs: Fine-tuning Mistral with LoRA

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As large language models (LLMs) continue to advance, there is a growing need to distill their knowledge into smaller, more efficient models suitable for real-world applications. One promising approach is knowledge distillation via fine-tuning using techniques like LoRA (Low-Rank Adaptation). In this article, we’ll dive into best practices for fine-tuning the 7B parameter Mistral model with LoRA.

The LoRA Advantage

Traditional fine-tuning updates all the weights of a pre-trained LLM, which can be computationally expensive and data-hungry, especially for large models. LoRA circumvents this by injecting trainable rank decomposition matrices into the LLM layers, enabling efficient adaptation to new tasks without modifying the original model weights.

Compared to full fine-tuning, LoRA requires significantly less compute and data, making it well-suited for fine-tuning models like Mistral. It has been shown to match or even exceed the performance of full fine-tuning on various tasks while using orders of magnitude fewer trainable parameters.

Selecting the Optimal LoRA Rank

The LoRA rank (r) determines the number of trainable parameters and directly impacts the model’s capacity to capture task-specific knowledge. A higher rank allows the model to better approximate the ideal fine-tuned weights, potentially improving performance. However, it also increases memory requirements and the risk of overfitting.

For Mistral, common ranks used are r=64 or r=128, though some have experimented with higher values like r=256 which can finetune around 8% of the model’s parameters. The optimal rank depends on the complexity of the task and dataset size – simple tasks may work well with lower r, while more complex ones may benefit from higher r.

Dataset Size and Quality

While LoRA is data-efficient compared to full fine-tuning, having sufficient high-quality training data is still crucial for achieving good performance. For a 7B model like Mistral, researchers recommend at least 50,000 examples for reasonable results, with 100,000+ examples often yielding better performance.

However, even smaller datasets of 1,000 – 10,000 carefully curated examples can be effective when using LoRA, outperforming full fine-tuning which requires much more data. Data quality and relevance to the target task are more important than sheer quantity – high-quality, curated datasets can outperform larger, noisier ones.

Using too little data (e.g. less than 1,000 examples) may lead to overfitting or poor performance. For very large datasets (>1M examples), full fine-tuning may be more effective than LoRA, depending on available compute resources.

Putting it All Together

So, what are the best practices for fine-tuning Mistral with LoRA? Based on current research, a good starting point could be:

  • LoRA rank (r) = 128
  • 10,000 – 100,000 high-quality, task-relevant examples

During training, it’s essential to monitor performance on a held-out validation set to select the best checkpoint and avoid overfitting. Additionally, increasing the LoRA alpha (lora_alpha) can help counteract a lower rank but may introduce instability.

Distillation Approaches

Beyond LoRA, researchers have explored various distillation approaches for transferring knowledge from large LLMs to smaller models:

  1. Reverse KL Divergence: Replacing the standard forward KL divergence loss with reverse KL can prevent the student model from overestimating low-probability regions of the teacher LLM’s distribution, making it more suitable for generative tasks.
  2. Multi-Task Learning with Rationales: Training the student on two tasks – label prediction and rationale generation, where rationales are intermediate reasoning steps extracted from the LLM teacher. This creates an explicit connection between inputs and outputs.
  3. Data Augmentation: Leveraging data augmentation to generate context-rich, skill-specific training data from the LLM teacher. This helps the student model approximate the teacher’s contextual abilities and ethical alignment.

The Future of LLM Distillation

As LLMs continue to grow in size and capability, techniques like LoRA and knowledge distillation will become increasingly important for making these models accessible and deployable across a wide range of applications.

By following best practices, leveraging the latest research, and adhering to legal and ethical considerations when working with LLM outputs, practitioners can effectively distill the knowledge from large models like Mistral into smaller, more efficient models tailored to their specific needs.

The possibilities for LLM distillation are vast, paving the way for a future where the power of large language models is available to everyone, regardless of computational resources.

Revolutionizing AI Efficiency: How Microsoft’s LLMLingua-2 is Changing the Game with 8x Less Memory

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  • LLMLingua-2 is a novel compression technology developed by Microsoft Research, achieving state-of-the-art results with 8 times less GPU memory on tasks typically handled by models like GPT-4.
  • It introduces innovative approaches such as « Data Distillation, » « Bidirectional Token Classification, » and optimized compression objectives to efficiently compress prompts without losing key information.
  • The technology has shown superior performance across various language tasks and demonstrated remarkable generalization across different LLMs and languages, from GPT-3.5 to Mistral-7B and from English to Chinese.
  • Compared to existing prompt compression methods, LLMLingua-2 is 3 to 6 times faster, accelerates end-to-end inference by 1.6 to 2.9 times, and significantly reduces GPU memory usage by a factor of 8.
  • This advancement represents a significant step forward in making language AI more practical and scalable for real-world applications, demonstrating Microsoft Research’s leadership in the field.

https://arxiv.org/pdf/2403.12968.pdf

sample

https://huggingface.co/microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank

Unified Time Series Model

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UniTS is a unified time series model that can process various tasks across multiple domains with shared parameters and does not have any task-specific modules.

Foundation models, especially LLMs, are profoundly transforming deep learning. Instead of training many task-specific models, we can adapt a single pretrained model to many tasks via few-shot prompting or fine-tuning. However, current foundation models apply to sequence data but not to time series, which present unique challenges due to the inherent diverse and multi-domain time series datasets, diverging task specifications across forecasting, classification and other types of tasks, and the apparent need for task-specialized models. 

We developed UniTS, a unified time series model that supports a universal task specification, accommodating classification, forecasting, imputation, and anomaly detection tasks. This is achieved through a novel unified network backbone, which incorporates sequence and variable attention along with a dynamic linear operator and is trained as a unified model. 

Across 38 multi-domain datasets, UniTS demonstrates superior performance compared to task-specific models and repurposed natural language-based LLMs. UniTS exhibits remarkable zero-shot, few-shot, and prompt learning capabilities when evaluated on new data domains and tasks. We will release the source code and datasets.

https://arxiv.org/pdf/2403.00131v1.pdf

https://zitniklab.hms.harvard.edu/projects/UniTS/

https://github.com/mims-harvard/UniTS

Renumics Spotlight

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Spotlight helps you to understand unstructured datasets fast. You can create interactive visualizations from your dataframe with just a few lines of code. You can also leverage data enrichments (e.g. embeddings, prediction, uncertainties) to identify critical clusters in your data.

https://spotlight.renumics.com/

Revolutionizing AI Reading Comprehension: ReadAgent’s Breakthrough in Handling Documents with 20 Million Tokens

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  • Introduction to ReadAgent by Google DeepMind
  • Development of ReadAgent, an AI capable of understanding long texts beyond the limits of its language model.
  • Utilizes a human-like reading strategy to comprehend complex documents.
  • Challenges Faced by Language Models
  • Context length limitation: Fixed token processing capacity leading to performance decline.
  • Ineffective context usage: Decreased comprehension with increasing text length.
  • Features of ReadAgent
  • Mimics human reading by forming and using « gist memories » of texts.
  • Breaks down texts into smaller « episodes » and generates gist memories for each.
  • Looks up relevant episodes when needed for answering questions.
  • Performance Enhancements
  • Capable of understanding documents « 20 times longer » than its base language model.
  • Shows improved performance on long document question answering datasets:
    • QuALITY: Accuracy improved from 85.8% to 86.9%.
    • NarrativeQA: Rating increased by 13-32% over baselines.
    • QMSum: Rating improved from 44.96% to 49.58%.
  • Potential Applications
  • Legal contract review, scientific literature analysis, customer support, financial report summarization, automated online course creation.
  • Indicates the future potential of AI in mastering lengthy real-world documents through human-like reading strategies.

https://read-agent.github.io/

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LORAX

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Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs

LoRAX (LoRA eXchange) is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency.

https://github.com/predibase/lorax

Publié dans LLM